Who are Heidi?
Heidi is building an AI Care Partner that supports clinicians every step of the way, from documentation to delivery of care.
We exist to double healthcare’s capacity while keeping care deeply human. In 18 months, Heidi has returned more than 18 million hours to clinicians and supported over 73 million patient visits. Today, more than two million patient visits each week are powered by Heidi across 116 countries and over 110 languages.
Founded by clinicians, Heidi brings together clinicians, engineers, designers, scientists, creatives, and mathematicians, working with a shared purpose: to strengthen the human connection at the heart of healthcare.
Backed by nearly $100 million in total funding, Heidi is expanding across the USA, UK, Canada, and Europe, partnering with major health systems including the NHS, Beth Israel Lahey Health, MaineGeneral, and Monash Health, among others.
We move quickly where it matters and stay grounded in what’s proven, shaping healthcare’s next era. Ready for the challenge?
The Role
Working closely with our Engineering Manager, you’ll be a Senior LLMOps Engineer on the Model Platform team. You are a technical leader responsible for building and scaling the infrastructure that powers our entire model lifecycle.
Your mission is to build a robust, scalable, and reliable platform for deploying and managing our LLMs. You will lead the design and implementation of our LLMOps strategy, ensuring our AI engineers can move models from development to production seamlessly and efficiently.
You will combine your deep infrastructure knowledge with MLOps principles to solve the critical challenges of serving models at scale.
What you’ll do:
Lead LLMOps Platform Development: Lead the architecture, design, and implementation of our end-to-end LLMOps platform, from data ingestion and model training pipelines to production deployment and monitoring.
Automate the LLM Lifecycle: Build and maintain robust CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines to automate the testing, validation, and deployment of large language models.
Ensure Scalable and Reliable Deployment: Engineer highly available and scalable model serving solutions using modern infrastructure like Kubernetes, ensuring low latency and high throughput for our production services.
Partner with AI and Engineering Teams: Collaborate closely with AI research and engineering teams to understand their needs, streamline workflows, and create the tooling that accelerates their development cycles.
Establish MLOps Best Practices: Champion and implement best practices for model versioning, experiment tracking, monitoring, and governance across the organization.
Mentor and Guide: Mentor mid-level and junior engineers, sharing your deep expertise in infrastructure, automation, and operational excellence to foster a culture of reliability and scalability.
What we will look for:
You’ve a proven track record of designing, building, and maintaining MLOps or LLMOps infrastructure in a production environment.
You’ve previous hands-on experience building scalable, cloud-native infrastructure and platforms.
You’ve deployed and managed large-scale machine learning models in a production environment, with a deep understanding of the associated challenges.
You are considered an expert in Python, cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and Infrastructure as Code (e.g., Terraform, CloudFormation).
You have a deep and practical understanding of the entire machine learning lifecycle and the specific operational challenges of large language models.
You have the ability to translate complex engineering and research requirements into concrete, robust, and automated platform solutions.
A Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Bonus:
Experience with advanced model serving and optimization techniques (e.g., quantization, distillation, multi-model serving).
Experience with specialized MLOps frameworks like MLflow, Kubeflow, or Weights & Biases.
Contributions to open-source MLOps or infrastructure-related projects.
What do we believe in?
Heidi builds for the future of healthcare, not just the next quarter, and our goals are ambitious because the world’s health demands it. We believe in progress built through precision, pace, and ownership.
Live Forever - Every release moves care forward: measured, safe, and built to last. Data guides us, but patients define the truth that matters.
Practice Ownership - Decisions follow logic and proof, not hierarchy. Exceptional care demands exceptional standards in our work, our thinking, and our character.
Small Cuts Heal Faster - Stability earns trust, speed delivers impact. Progress is about learning fast without breaking what people depend on.
Make others better - Feedback is direct, kindness is constant, and excellence lifts everyone. Our success is measured by collective growth, not individual output.
Our mission is clear: expand the world’s capacity to care, and do it without losing the humanity that makes care worth delivering.
Why you will flourish with us 🚀?
Flexible hybrid working environment, with 3 days in the office.
Additional paid day off for your birthday and wellness days
Special corporate rates at Anytime Fitness in Melbourne, Sydney tbc.
A generous personal development budget of $500 per annum
Learn from some of the best engineers and creatives, joining a diverse team
Become an owner, with shares (equity) in the company, if Heidi wins, we all win
The rare chance to create a global impact as you immerse yourself in one of Australia’s leading healthtech startups
If you have an impact quickly, the opportunity to fast track your startup career!
Help us reimagine primary care and change the face of healthcare in Australia and then around the world.
